D-lactic acid biosynthesis from biomass-derived sugars via Lactobacillus delbrueckii fermentation

Poly-lactic acid (PLA) derived from renewable resources is considered to be a good substitute for petroleum-based plastics. The number of poly L-lactic acid applications is increased by the introduction of a stereocomplex PLA, which consists of both poly-L and D-lactic acid and has a higher melting temperature. To date, several studies have explored the production of L-lactic acid, but information on biosynthesis of D-lactic acid is limited. Pulp and corn stover are abundant, renewable lignocellulosic materials that can be hydrolyzed to sugars and used in biosynthesis of D-lactic acid. In our study, saccharification of pulp and corn stover was done by cellulase CTec2 and sugars generated from hydrolysis were converted to D-lactic acid by a homofermentative strain, L. delbrueckii, through a sequential hydrolysis and fermentation process (SHF) and a simultaneous saccharification and fermentation process (SSF). 36.3 g Lˉ¹ of D-lactic acid with 99.8 % optical purity was obtained in the batch fermentation of pulp and attained highest yield and productivity of 0.83 g gˉ¹ and 1.01 g Lˉ¹ hˉ¹, respectively. Luedeking–Piret model described the mixed growth-associated production of D-lactic acid with a maximum specific growth rate 0.2 hˉ¹ and product formation rate 0.026 hˉ¹, obtained for this strain. The efficient synthesis of D-lactic acid having high optical purity and melting point will lead to unique stereocomplex PLA with innovative applications in polymer industry

Optically pure D (-) lactic acid biosynthesis from diverse renewable biomass: microbial strain development and bioprocess analysis

Doctor of PhilosophyDepartment of Grain Science and IndustryPraveen V. VadlaniLactic acid is an important platform chemical that has long history and wide applications in food, polymer, pharmaceutics and cosmetic industries. Lactic acid has two optical isomers; namely D-lactic acid and L-lactic acid. Racemic mixture of lactic acid are usually used as preservatives and ingredients in solvents, or as precursors for different chemicals. Currently there is an increasing demand of optical pure lactic acid as a feedstock for the production of poly-lactic acid (PLA). PLA is a biodegradable, biocompatible and environmental friendly alternative to plastics derived from petroleum based chemicals. Optically pure D or L-lactic acid is used for the synthesis of poly D or L- lactic acid (PDLA, PLLA). Blend of PDLA with PLLA results in a heat-resistant stereocomplex PLA with excellent properties. As a consequence, large quantity of cost effective D-lactic acid is required to meet the demand of stereocomplex PLA. Lignocellulosic biomass is a promising feedstock for lactic acid production because of its availability, sustainability and cost effectiveness compared to refined sugars and cereal grain-based sugars. Commercial use of lignocellulosic biomass for economic production of lactic acid requires microorganisms that are capable of using all sugars derived from lignocellulosic biomass. Therefore, the objectives of this study were: 1) to produce high level of optically pure D-lactic acid from lignocellulosic biomass-derived sugars using a homofermentative strain L. delbrueckii via simultaneous saccharification and fermentation (SSF); 2) to develop a co-culture fermentation system to produce lactic acid from both pentose and hexose sugars derived from lignocellulosic biomass; 3) to produce D-lactic acid by genetically engineered L. plantarum NCIMB 8826 ∆ldhL1 and its derivatives; 4) to construct recombinant L. plantarum by introduction of a plasmid (pLEM415-xylAB) used for xylose assimilation and evaluate its ability to produce D-lactic acid from biomass sugars; and 5) to perform metabolic flux analysis of carbon flow in Lactobacillus strains used in our study. Our results showed that D-lactic acid yield from alkali-treated corn stover by L. delbrueckii and L. plantarum NCIMB 8826 ∆ldhL1 via SSF were 0.50 g g[superscript]-1 and 0.53 g g[superscript]-1 respectively; however, these two D-lactic acid producing strains cannot use xylose from hemicellulose. Complete sugar utilization was achieved by co-cultivation of L. plantarum ATCC 21028 and L. brevis ATCC 367, and lactic acid yield increased to 0.78 g g[superscript]-1 from alkali-treated corn stover, but this co-cultivation system produced racemic mixture of D and L lactic acid. Simultaneous utilization of hexose and pentose sugars derived from biomass was achieved by introduction of two plasmids pCU-PxylAB and pLEM415-xylAB carrying xylose assimilation genes into L. plantarum NCIMB 8826 ∆ldhL1, respectively; the resulting recombinant strains ∆ldhL1-pCU-PxylAB and ∆ldhL1-pLEM415-xylAB used xylose and glucose simultaneously and produced high yield of optically pure D-lactic acid. Metabolic flux analysis verified the pathways used in these Lactobacillus strains and provided critical information to judiciously select the desired Lactobacillus strain to produce lactic acid catering to the composition of raw material and the optical purity requirement. This innovative study demonstrated strategies for low-cost biotechnological production of tailor-made lactic acid from specific lignocellulosic biomass, and thereby provides a foundational manufacturing route for a flexible and sustainable biorefinery to cater to the fuel and chemical industry

Melodic Phrase Segmentation By Deep Neural Networks

Automated melodic phrase detection and segmentation is a classical task in content-based music information retrieval and also the key towards automated music structure analysis. However, traditional methods still cannot satisfy practical requirements. In this paper, we explore and adapt various neural network architectures to see if they can be generalized to work with the symbolic representation of music and produce satisfactory melodic phrase segmentation. The main issue of applying deep-learning methods to phrase detection is the sparse labeling problem of training sets. We proposed two tailored label engineering with corresponding training techniques for different neural networks in order to make decisions at a sequential level. Experiment results show that the CNN-CRF architecture performs the best, being able to offer finer segmentation and faster to train, while CNN, Bi-LSTM-CNN and Bi-LSTM-CRF are acceptable alternatives

Novel feedback-Bayesian BP neural network combined with extended Kalman filtering for the battery state-of-charge estimation.

The state of charge estimation of lithium-ion batteries plays an important role in real-time monitoring and safety. To solve the problem that high non-linearity during real-time estimation of lithium-ion batteries who cause that it is difficult to estimate accurately. Taking lithium-ion battery as the research object, the working characteristics of lithium-ion ion battery are studied under various working conditions. To reduce the error caused by the nonlinearity of the lithium battery system, the BP neural network with the high approximation of nonlinearity is combined with the extended Kalman filtering. At the same time, to eliminate the over fitting of training, Bayesian regularization is used to optimize the neural network. Taking into account the real-time requirements of lithium-ion batteries, a feedback network is adopted to carry out real-time algorithm integration on lithium-ion batteries. A simulation model is established, and the results are analyzed in combination with various working conditions. Experimental results show that the algorithm has the characteristics of fast convergence and good tracking effect, and the estimation error is within 1.10%. It is verified that the Feedback-Bayesian BP neural network combined with the extended Kalman filtering algorithm can improve the accuracy of lithium-ion battery state-of-charge estimation